Balancing Immediate Revenue and Future Off-Policy Evaluation in Coupon Allocation

• URL: http://arxiv.org/abs/2407.11039v2
• Date: Wed, 17 Jul 2024 07:02:41 GMT
• Title: Balancing Immediate Revenue and Future Off-Policy Evaluation in Coupon Allocation
• Authors: Naoki Nishimura, Ken Kobayashi, Kazuhide Nakata,
• Abstract summary: A/B testing can validate new policies, but it risks compromising short-term revenue. We propose a novel approach that combines a model-based revenue policy and a randomized exploration policy for data collection. We empirically verified the effectiveness of the proposed mixed policy using both synthetic and real-world data.
• Score: 3.4562366784858685
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Coupon allocation drives customer purchases and boosts revenue. However, it presents a fundamental trade-off between exploiting the current optimal policy to maximize immediate revenue and exploring alternative policies to collect data for future policy improvement via off-policy evaluation (OPE). While online A/B testing can validate new policies, it risks compromising short-term revenue. Conversely, relying solely on an exploitative policy hinders the ability to reliably estimate and enhance future policies. To balance this trade-off, we propose a novel approach that combines a model-based revenue maximization policy and a randomized exploration policy for data collection. Our framework enables flexibly adjusting the mixture ratio between these two policies to optimize the balance between short-term revenue and future policy improvement. We formulate the problem of determining the optimal mixture ratio between a model-based revenue maximization policy and a randomized exploration policy for data collection. We empirically verified the effectiveness of the proposed mixed policy using both synthetic and real-world data. Our main contributions are: (1) Demonstrating a mixed policy combining deterministic and probabilistic policies, flexibly adjusting the data collection vs. revenue trade-off. (2) Formulating the optimal mixture ratio problem as multi-objective optimization, enabling quantitative evaluation of this trade-off. By optimizing the mixture ratio, businesses can maximize revenue while ensuring reliable future OPE and policy improvement. This framework is applicable in any context where the exploration-exploitation trade-off is relevant.

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